Self-Organisation of Evolving Agent Populations in Digital Ecosystems

TL;DR

This paper explores the self-organising behavior of Digital Ecosystems by extending biological and computer science concepts to measure complexity, stability, and diversity, supported by experimental results.

Contribution

It introduces new definitions for complexity, stability, and diversity in Digital Ecosystems, grounded in biological and computational theories, with experimental validation.

Findings

Digital Ecosystems exhibit self-organisation properties.

Extended measures effectively quantify complexity, stability, and diversity.

Experimental results support the theoretical framework.

Abstract

We investigate the self-organising behaviour of Digital Ecosystems, because a primary motivation for our research is to exploit the self-organising properties of biological ecosystems. We extended a definition for the complexity, grounded in the biological sciences, providing a measure of the information in an organism's genome. Next, we extended a definition for the stability, originating from the computer sciences, based upon convergence to an equilibrium distribution. Finally, we investigated a definition for the diversity, relative to the selection pressures provided by the user requests. We conclude with a summary and discussion of the achievements, including the experimental results.